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The purpose of this paper is to investigate the perceptions that managers have of the value and reliability of using big data to make hotel revenue management and pricing decisions.

A three-stage iterative thematic analysis technique based on the approaches of Braun and Clarke (2006) and Nowell et al. (2017) and using different research instruments to collect and analyse qualitative data at each stage was used to develop an explanatory framework.

Whilst big data-driven automated revenue systems are technically capable of making pricing and inventory decisions without user input, the findings here show that the reality is that managers still interact with every stage of the revenue and pricing process from data collection to the implementation of price changes. They believe that their personal insights are as valid as big data in increasing the reliability of the decision-making process. This is driven primarily by a lack of trust on the behalf of managers in the ability of the big data systems to understand and interpret local market and customer dynamics.

The less a manager believes in the ability of those systems to interpret these data, the more they perceive gut instinct to increase the reliability of their decision making and the less they conduct an analysis of the statistical data provided by the systems. This provides a clear message that there appears to be a need for automated revenue systems to be flexible enough for managers to import the local data, information and knowledge that they believe leads to revenue growth.

There is currently little research explicitly investigating the role of big data in decision making within hotel revenue management and certainly even less focussing on decision making at property level and the perceptions of managers of the value of big data in increasing the reliability of revenue and pricing decision making.

This paper aims to overcome some of the limitations of previous works regarding automated supply chain formation (SCF). Hence, it proposes an algorithm for automated SCF using multiple contract parameters. Moreover, it proposes a decision-making mechanism that provides means for incorporating risk in the decision-making process. To better emphasize the features of the proposed decision-making mechanism, the paper provides some insights from the petroleum industry. This industry has a strategic position, as it is the base for other essential activities of the economy of any country. The petroleum industry is faced with volatile feed-stock costs, cyclical product prices and seasonal final products demand.

The authors have modeled the supply chain in terms of a cluster graph where the nodes are represented by clusters over the contract parameters that suppliers/consumers are interested in. The suppliers/consumers own utility functions and agree on multiple contract parameters by message exchange, directly with other participant agents, representing their potential buyer or seller. The agreed values of the negotiated issues are reflected in a contract which has a certain utility value for every agent. They consider uncertainties in crude oil prices and demand in petrochemical products and model the decision mechanism for a refinery by using an influence diagram.

By integrating the automated SCF algorithm and a mechanism for decision support under uncertainty, the authors propose a reliable and practical decision-making model with a practical application not only in the petroleum industry but also in any other complex industry involving a multi-tier supply chain.

The limitation of this approach reveals in situations where the parameters can take values over continuous domains. In these cases, storing the preferences for every agent might need a considerable amount of memory depending on the size of the continuous domain; hence, the proposed approach might encounter efficiency issues.

The current paper makes a step forward to the implementation of digital supply chains in the context of Industry 4.0. The proposed algorithm and decision-making mechanism become powerful tools that will enable machines to make autonomous decisions in the digital supply chain of the future.

The current work proposes a decentralized mechanism for automated SCF. As opposed to the previous decentralized approaches, this approach translates the SCF optimization problem not as a profit maximization problem but as a utility maximization. Hence, it incorporates multiple parameters and uses utility functions to find the optimal supply chain. The current approach is closer to real life scenarios than the previous approaches that were using only cost as a mean for pairwise agents because it uses utility functions for entities in the supply chain to make decision. Moreover, this approach overcomes the limitations of previous approaches by providing means to incorporate risk in the decision-making mechanism.

The purpose of this study is to explore if and how informal governance mechanisms are used for coordinating actors of digitalized supply networks.

A multiple case study analysis of ten German firms in digitalized supply networks is conducted. Data are collected through semi-structured expert interviews with interviewees having notable years of professional experience in purchasing or sales.

This research shows that reduced personal contacts in digitalized supply networks decrease the ability to coordinate with informal governance mechanisms. However, the need for informal governance in digitalized supply networks remains when facing supply network disruptions or building new business relationships.

This is the first study examining the role of informal governance mechanisms in digitalized supply networks. Theoretical and practical insights on the changing abilities and needs to use informal governance after the digital transformation are given.

To remain competitive, efficient and productive, organisations need to ensure that their employees continuously learn and develop. This is even more challenging and critical in times characterised by volatility, uncertainty, complexity and ambiguity (VUCA). Hence, several technological applications have been introduced with the promise to make organisational training and development (T&D) more efficient and targeted through digitisation and automation. However, digitising and automating processes in the sensitive field of T&D also poses challenges and perils for employees and organisations as a whole.

Structured by the T&D process of (1) assessment/planning, (2) design/implementation and (3) evaluation, the authors present different digitisation and automation possibilities and discuss the specific opportunities and challenges they pose. Subsequently, the authors identify and discuss overarching themes of opportunities and challenges of technology use in T&D via a meta-review.

This synthesis revealed three central topics that decision-makers in T&D should carefully consider when it comes to the implementation of technological applications: opportunities and challenges of (1) data collection, (2) decision-making and (3) the value of human contact.

This review integrates previously fragmented research on specific technologies applied to specific T&D functions and provides researchers and practitioners with a fuller picture of the opportunities and challenges of technology applied in T&D.

The purpose of this paper is to present the legal conditions under which governments may use green artificial intelligence (AI) in city planning. Although Japan was one of the early countries to release its general AI principles, it has been relatively slow in establishing conditions where administrative agencies may use AI. Granted, there have been some recent scholarship that discusses the usage of AI in general under Japanese administrative law, but the use of green AI in city planning under Japanese law has not yet been discussed. Hence, this paper intends to focus on green AI in city planning and discuss the conditions for usage based on different categories of AI.

This paper conducts a legal analysis on the utilization of AI for the purpose of sustainable city planning and administration in Japan. The approach of this paper is to summarize the existing scholarship in Japanese administrative law and analyse the new elements in the new field of green AI in city planning. This paper is not a natural science paper. The social science method of jurisprudence is used. This paper cites only public sources, and no informal literature has been referenced.

This paper establishes the conditions where Japanese central and local government may use green AI in city planning from a legal viewpoint based on three categories. The categories are green AI usage in city planning concerning things, green AI usage in city planning concerning people and green AI usage in city planning concerning automated decision-making.

This research is limited to an analysis of Japanese law, which means that issues other than law are not included in this paper. Further, although general legal issues are discussed, this paper is intended to discuss Japanese law issues only, and foreign laws are not discussed. Therefore, this paper mostly cites Japanese language papers published in domestic journals.

The intended practical implication of this paper is to allow central and local governments to determine – based on the proposed categories – whether green AI can be used for city planning purposes and under which conditions. The authors hope that this will assist the Japanese government in establishing rules on the usage of AI by governmental agencies and allow for the greater actual usage by Japanese central and local governments of green AI in future city planning.

As the theme of this paper deals with governmental use (and the function of a government is to serve society), the social implications at issue can be said to be equivalent to the practical implication.

There have been articles discussing Japanese administrative law restrictions on AI in general. However, as of now, to the best of the authors’ knowledge, there have been no articles published focusing on green AI used for city planning. The authors note that the green AI used for city planning would have different legal implications from AI’s usage by the government in general, such as the chatbot used by the agencies or lethal autonomous weapons by the military force. Therefore, this paper is original in focusing on green AI used for city planning.

The penetration of technology and the strengthening of evidence-based policies have paved the way for the automated delivery of social services. This study aims to discuss the inherent risks of this automatization, particularly those associated with the discrimination, exclusion and inequality problem, which the authors package under the theoretical umbrella of a digital welfare state (DWS).

This conceptual article reviews the literature on the welfare DWS, with an empirical focus on the recent experience of selected countries from India, Kenya and Sweden. These countries reflect three different types of welfare regimes but are connected by the same digital social risk. The authors’ exploration also includes questions about what this DWS has in common with and how it differs from the previous era. This article illustrates that there has been a very similar trajectory in regards to the development of the DWS and the associated risks in the examined countries.

DWS has triggered new social risks (e.g. discrimination, exclusion and inequality in welfare access) that are a result of data breaches experienced by citizens. Further, vulnerable groups in the digital age should be viewed not only as those who lack access to welfare services, such as education, health and employment, but also as those without internet access, without digital skills and excluded from the DWS system.

The article calls for the development of scholarly research into the DWS in particular and the contemporary one in general. The authors also predict that a critical aspect of the future regime typology rests in the ability to mobilize resources to address contemporary digital risks, as every country is equally vulnerable to them. Overall, this article can be considered to be one of the initial works that focus on cross-national comparison across different meta-welfare regimes.

To: evaluate Prospect Theory and Cumulative Prospect Theory as functional models of decision making and risk within various contexts; compare and analyze risk models and decision‐making models; evaluate models of stock risk developed by Robert Engle and related models; establish whether the models are related and have the same foundations; relate risk, decision making and options theory; and develop the foundations for a new model of decision making and risk named “belief systems”.

Critiques existing academic work in different contexts. Analyzes the shortcomings of various measures of risk, and group decision making, which was not addressed in developing Prospect Theory and Cumulative Prospect Theory. Develops the characteristics of a mew model for decision making and risk named “belief systems”, and then differentiates it from belief networks.

Decision making is a multi‐factor, multi‐dimensional process that often requires the processing of information, and thus, it is inaccurate to impose rigid models in decision making; the existing metrics for quantifying risk are inadequate; Prospect Theory and Cumulative Prospect Theory were developed using questionable methods and data, and are impractical; the analysis of probabilistic insurance and most of the theories and “effects” developed by Kahneman and Tversky's articles are invalid and impractical; Prospect Theory, Cumulative Prospect Theory, Expected Utility Theory, and market‐risk models are conceptually the same and do not account for many facets of risk and decision making; risk and decision making are better quantified and modeled using a mix of situation‐specific dynamic, quantitative and qualitative factors; belief systems can better account for the multi‐dimensional characteristics of risk and decision making.

Areas for further research include: development of dynamic market‐risk models that incorporate asset‐market psychology, liquidity, market size, frequency of trading, knowledge differences among market participants, and trading rules in each market; and further development of concepts in belief systems.

Decision making and risk assessment are multi‐criteria processes that typically require some processing of information, and thus cannot be defined accurately by rigid quantitative models; Prospect Theory and Cumulative Prospect Theory are abstract, rigid, and are not practical models for decision making; and existing market‐risk models are inaccurate, and thus the international financial system may be compromised.

The issues discussed are relevant to government regulators, central banks, judges, risk managers, executives, derivatives regulators, stock exchange regulators, legislators, psychologists, boards of directors, finance professionals, management science/operations research professionals, health‐care‐informatics professionals, scientists, engineers, and people in any situation that requires decision making and risk assessment.

Artificial intelligence (AI) applications’ usage in Chief Officers’ (CXOs’) decision-making is a topic of current research interest. A fundamental dilemma is carefully planning an effective combination of a CXO’s professional experiences and AI applications’ decision-making responsibility. However, the existing literature fails to specify the value of co-creation of AI applications and the human experience in managerial decision-making. To address this gap in the literature, the authors’ examine how an ideal cognitive-technology fit can be created between human experiences and AI-based solutions at CXO-level decision-making using the theoretical lens of the Service-Dominant Logic.

The authors’ employed a grounded theory approach and conducted a focus group discussion with seven participants to shed light on the factors that may balance AI applications’ usage and CXOs’ experience in making business decisions. This was followed by 21 in-depth interviews with employees from knowledge-intensive professional service firms to validate the findings further of a new phenomenon. Further, given the newness of the phenomenon, this approach allowed researchers a retrospective and real-time understanding of interviewees’ experiences of the phenomenon under consideration.

The advantages and constraints of both CXOs’ experiences and AI applications deserve due consideration for successfully implementing technology in knowledge-intensive professional service organizations.

This study may appeal to researchers and practitioners interested in the future of decision-making, as the authors’ study findings advocate for balancing CXO’s expertise and the use of AI in decision-making.

Based on the preliminary findings, the authors developed a theoretical framework to understand the factors that govern AI implementation in an organization and how a competitive strategy may emerge from value co-created by AI applications and CXOs’ experience, particularly in knowledge-intensive professional service firms.

This chapter considers how social capital is evolving in the era of globalization today especially under COVID-19 pandemic conditions globally. Definitions of social capital have varied: some broad others narrow. The Organisation for Economic Co-operation and Development (OECD), for example, currently has a broad research project on social capital. These researchers have defined social capital as comprising four key areas. These areas are:

• Personal relationships, referring to the structure of people’s social networks.

• Depth and breadth of social network support available to each person in their networks.

• Civic engagement activities such as volunteering and community action.

• Beliefs, attitudes, and action frames of reference such as trust and cooperative norms, of reciprocity.

Personal relationships, referring to the structure of people’s social networks.

Depth and breadth of social network support available to each person in their networks.

Civic engagement activities such as volunteering and community action.

Beliefs, attitudes, and action frames of reference such as trust and cooperative norms, of reciprocity.

Thus, there are tacit as well as explicit aspects of social capital though some of these may seldom if ever be articulated and delineated for others.

As Claridge (2020) indicates, there are distinct, but dynamically interrelated, levels of social capital. These levels range between the micro- or individual level. That is personal “habitus” – which Bourdieu (1977) describes as a person’s “taken-for-granted” – ways of being, thinking, and reacting to events and to other people. Then, the next level above the individual is the meso-level, which is “how things are done here amongst us,” that is, the level of a group’s social capital (such as a team, or an organizational or local community level). Lastly, and wider still, the top level is the macro- or cultural-societal structural level of the nation.

The social capital systems in any location encompasses sets of acceptable or culturally legitimated behavioral norms and rules of engagement between community members which include types of greetings, forms of cooperation, communications, and signaling between diverse members. Thus, social capital may be present in the tacit, or unspoken/taken-for-granted assumptions as much as in explicit or formalized codes of behavior. The forms of social interactions at each of the levels may have norms for specific types communication and address in particular sets of circumstances such as social gatherings at home or in public or when attending communal gatherings or ceremonial occasions, or between people of different social status. Social capital generates trust and social cohesion and some level of cultural and attitudinal consensus and interest, which in turn delivers a stable environment for the local community or larger society, business, or the economy.

• (1)

  Social capital is the development of relationships that help contribute to a more efficient production of goods and services as there is embedded trust, embodied in practice, that is, in behaviors regarded as trustworthy and socially helpful.

• (2)

  There are three types of social capital at each level of interaction – bonding, bridging, and linking. Bridging and linking are similar though they operate in different directions socially. Bonding social capital describes the connections between people in similar social levels or groups of people who share the same characteristic norms and beliefs, whereas linking social capital facilitates connects between different groups.

• (3)

  Social capital can therefore make or break businesses, especially small businesses or start-ups as those with the right kind and amount of social capital, such as good connections and contacts in the trade or profession, can usually thrive as they are able to get work done more quickly, effectively, and efficiently. Conversely, a lack of social capital denoting some distrust between groups can undermine social stability.

Social capital is the development of relationships that help contribute to a more efficient production of goods and services as there is embedded trust, embodied in practice, that is, in behaviors regarded as trustworthy and socially helpful.

There are three types of social capital at each level of interaction – bonding, bridging, and linking. Bridging and linking are similar though they operate in different directions socially. Bonding social capital describes the connections between people in similar social levels or groups of people who share the same characteristic norms and beliefs, whereas linking social capital facilitates connects between different groups.

Social capital can therefore make or break businesses, especially small businesses or start-ups as those with the right kind and amount of social capital, such as good connections and contacts in the trade or profession, can usually thrive as they are able to get work done more quickly, effectively, and efficiently. Conversely, a lack of social capital denoting some distrust between groups can undermine social stability.

The meso- or macro-levels of bridging type social capital ensures acceptance of established social roles locally and linking forms of social capital boost levels of acceptance of other roles such as those of leaders and followers.

All three forms of social capital and the three levels are not mutually exclusive but instead are mutually inclusive and interrelated. That is, they co-evolve, each impacting the other while dynamically interacting with the social capital anchored as it is emerging from the complex and interwoven fields of tacit and explicit norms of social interaction underpinning each of the levels of interaction over time.

This paper aims to analyse three decision-making approaches that involve humans and artificial autonomous agents, namely, human “in the loop”, “on the loop” and “out of the loop” and identifies the decision characteristics that determine the choice of a decision-making approach.

This is a conceptual paper that analyses the relationships between the human and the artificial autonomous agents in the decision-making process from the perspectives of the agency theory, sustainability, legislation, economics and operations management.

The paper concludes that the human “out of the loop” approach is most suitable for quick, standardised, frequent decisions with low negative consequences of a wrong decision by the artificial intelligence taken within a well-defined context. Complex decisions with high outcome uncertainty that involve significant ethical issues require human participation in the form of a human “in the loop” or “on the loop” approach. Decisions that require high transparency need to be left to humans.

The paper evaluates the decision-making approaches from the perspectives of the agency theory, sustainability, legislation, economics and operations management and identifies the decision characteristics that determine the choice of a decision-making approach.